Neural Ordinary Differential Equations (Neural ODEs) are a type of machine learning model that use the mathematics of continuous change to process information. Instead of stacking discrete layers like typical neural networks, Neural ODEs treat the transformation of data as a smooth, continuous process described by differential equations. This allows them to model complex systems…
Category: Model Training & Tuning
Knowledge Tracing
Knowledge tracing is a technique used to monitor and predict a learner’s understanding of specific topics or skills over time. It uses data from quizzes, homework, and other activities to estimate how much a student knows and how likely they are to answer future questions correctly. This helps teachers and learning systems personalise instruction to…
Domain Adaptation
Domain adaptation is a technique in machine learning where a model trained on data from one environment or context is adjusted to work well in a different but related environment. This is useful when collecting labelled data for every new situation is difficult or expensive. Domain adaptation methods help models handle changes in data, such…
Active Learning
Active learning is a machine learning method where the model selects the most useful data points to learn from, instead of relying on a random sample of data. By choosing the examples it finds most confusing or uncertain, the model can improve its performance more efficiently. This approach reduces the amount of labelled data needed,…
Meta-Learning
Meta-learning is a method in machine learning where algorithms are designed to learn how to learn. Instead of focusing on solving a single task, meta-learning systems aim to improve their ability to adapt to new tasks quickly by using prior experience. This approach helps machines become more flexible, allowing them to handle new problems with…
Bayesian Neural Networks
Bayesian Neural Networks are a type of artificial neural network that use probability to handle uncertainty in their predictions. Instead of having fixed values for their weights, they represent these weights as probability distributions. This approach helps the model estimate not just an answer, but also how confident it is in that answer, which can…
Ghost Parameter Retention
Ghost Parameter Retention refers to the practice of keeping certain parameters or settings in a system or software, even though they are no longer in active use. These parameters may have been used by previous versions or features, but are retained to maintain compatibility or prevent errors. This approach helps ensure that updates or changes…
Subsymbolic Feedback Tuning
Subsymbolic feedback tuning is a process used in artificial intelligence and machine learning where systems adjust their internal parameters based on feedback, without relying on explicit symbols or rules. This approach is common in neural networks, where learning happens through changing connections between units rather than following step-by-step instructions. By tuning these connections in response…
Zero Resource Learning
Zero Resource Learning is a method in artificial intelligence where systems learn from raw data without needing labelled examples or pre-existing resources like dictionaries. Instead of relying on human-annotated data, these systems discover patterns and structure by themselves. This approach is especially useful for languages or domains where labelled data is scarce or unavailable.
Gradient Clipping
Gradient clipping is a technique used in training machine learning models to prevent the gradients from becoming too large during backpropagation. Large gradients can cause unstable training and make the model’s learning process unreliable. By setting a maximum threshold, any gradients exceeding this value are scaled down, helping to keep the learning process steady and…